Advantages of tandem LC-MS for the rapid assessment of tissue-specific metabolic complexity using a pentafluorophenylpropyl stationary phase

In this study, a tandem LC-MS (Waters Xevo TQ) MRM-based MS method was developed for rapid, broad profiling of hydrophilic metabolites from biological samples, in either positive or negative ion modes without the need for an ion pairing reagent, using a reversed-phase pentafluorophenylpropyl (PFPP) column. The developed method was successfully applied to analyze various biological samples from C57BL/6 mice, including urine, duodenum, liver, plasma, kidney, heart, and skeletal muscle. As result, a total 112 of hydrophilic metabolites were detected within 8 min of running time to obtain a metabolite profile of the biological samples. The analysis of this number of hydrophilic metabolites is significantly faster than previous studies. Classification separation for metabolites from different tissues was globally analyzed by PCA, PLS-DA and HCA biostatistical methods. Overall, most of the hydrophilic metabolites were found to have a "fingerprint" characteristic of tissue dependency. In general, a higher level of most metabolites was found in urine, duodenum, and kidney. Altogether, these results suggest that this method has potential application for targeted metabolomic analyzes of hydrophilic metabolites in a wide ranges of biological samples.

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Innate transcriptional networks activated in bladder in response to uropathogenic Escherichia coli drive diverse biological pathways and rapid synthesis of IL-10 for defense against bacterial urinary tract infection

Early transcriptional activation events that occur in bladder immediately following bacterial urinary tract infection (UTI) are not well defined. In this study, we describe the whole bladder transcriptome of uropathogenic Escherichia coli (UPEC) cystitis in mice using genome-wide expression profiling to define the transcriptome of innate immune activation stemming from UPEC colonization of the bladder. Bladder RNA from female C57BL/6 mice, analyzed using 1.0 ST-Affymetrix microarrays, revealed extensive activation of diverse sets of innate immune response genes, including those that encode multiple IL-family members, receptors, metabolic regulators, MAPK activators, and lymphocyte signaling molecules. These were among 1564 genes differentially regulated at 2 h postinfection, highlighting a rapid and broad innate immune response to bladder colonization. Integrative systems-level analyses using InnateDB (http://www.innatedb.com) bioinformatics and ingenuity pathway analysis identified multiple distinct biological pathways in the bladder transcriptome with extensive involvement of lymphocyte signaling, cell cycle alterations, cytoskeletal, and metabolic changes. A key regulator of IL activity identified in the transcriptome was IL-10, which was analyzed functionally to reveal marked exacerbation of cystitis in IL-10–deficient mice. Studies of clinical UTI revealed significantly elevated urinary IL-10 in patients with UPEC cystitis, indicating a role for IL-10 in the innate response to human UTI. The whole bladder transcriptome presented in this work provides new insight into the diversity of innate factors that determine UTI on a genome-wide scale and will be valuable for further data mining. Identification of protective roles for other elements in the transcriptome will provide critical new insight into the complex cascade of events that underpin UTI.

Turbocharging Networks in the Public Sector

Networks have come to occupy a key position in the strategic armoury of the government, business and community sectors and now have impact on a broad array of policy and management arenas. An emphasis on relationships, trust and mutuality mean that networks function on a different operating logic to the conventional processes of government and business. It is therefore important that organizational members of networks are able to adopt the skills and culture necessary to operate successfully under these distinctive kinds of arrangements. Because networks function from a different operational logic to traditional bureaucracies, public sector organizations may experience difficulties in adapting to networked arrangements. Networks are formed to address a variety of social problems or meet capability gaps within organizations. As such they are often under pressure to quickly produce measurable outcomes and need to form rapidly and come to full operation quickly. This paper presents a theoretical exploration of how diverse types of networks are required for different management and policy situations and draws on a set of public sector case studies to understand/demonstrate how these various types of networked arrangements may be ‘turbo-charged’ so that they more quickly adopt the characteristics necessary to deliver required outcomes.